Assessing the Object-level behavioral complexity in Object Relational Databases

Selwyn, Justus; Iyakutti, K.

doi:10.1109/swste.2007.12

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…Physical database scheme quality control is impossible without numerical indicators [4], [8]. In the absence of quantitative measurements, it is difficult to make any design decisions.…”

Section: Problem Statementmentioning

confidence: 99%

Data Types Diversity Quantitative Indicators in Relational Databases Physical Models

Rybanov¹,

Aleksandrovna²

2020

TEM Journal

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The article compares diversity indices applicability used in ecology to assess data types distribution for physical database schemes. The following dominance measures are used to assess the data type's diversity in the physical database scheme: Simpson's diversity index, Shannon diversity index, Simpson's evenness index, Pielu evenness index. A comparative analysis of the data type's diversity indicators for physical database schemes showed that the values of some indices obey certain rules. It is proposed to use diversity indices for physical database schemes' qualitative assessment. The indices considered in this work can also be used to build new models for assessing database complexity.

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“…Physical database scheme quality control is impossible without numerical indicators [4], [8]. In the absence of quantitative measurements, it is difficult to make any design decisions.…”

Section: Problem Statementmentioning

confidence: 99%

Data Types Diversity Quantitative Indicators in Relational Databases Physical Models

Rybanov¹,

Aleksandrovna²

2020

TEM Journal

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“…The system adopts a semi-automatic prefetch technique based on user hints for performance improvement. Justus and Iyakutti [18][19][20] discussed various metrics for measuring static/dynamic status of an object database. In [18], two object-level metrics, called coupling and cohesion, were presented for measuring an object's runtime status.…”

Section: Related Workmentioning

confidence: 99%

“…9 shows the algorithm which returns an iterative pattern view V that best matches the given subgraph S of a TLAG T based on Definitions 1 and 2. The algorithm first finds all the iterative pattern views matching the given subgraph S in lines (1)- (16) and then selects an iterative pattern view with the minimal cost in lines (17)- (18). To find all the matching iterative pattern views, for each of the TLAGs T V for the iterative pattern views in the database, the algorithm checks if ASE (1) in Definition 2 is satisfied in the lines (3)-(7), and CASE (2) in lines (8)- (15).…”

Section: Rulementioning

confidence: 99%

Type-level access pattern view: Enhancing prefetching performance using the iterative and recursive patterns

Han

Loh

Whang

2010

Information Sciences

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“…dynamic or runtime metrics) (2) the static nature of the system which reflects the internal quality and that can be measured at the design time (i.e. static metrics) [7].…”

Section: Introductionmentioning

confidence: 99%

Automating the Collection of Object Relational Database Metrics

Suleiman¹,

Al-Radaideh²,

Abul-Huda³

et al. 2011

IJACSA

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Abstract-The quality of software systems is the most important factor to consider when designing and using these systems. The quality of the database or the database management system is particularly important as it is the backbone for all types of systems that it holds their data. Many researches argued that software with high quality will lead to an effective and secure system. Software quality can be assessed by using software measurements or metrics. Typically, metrics have several problems such as: having no specific standards, sometimes they are hard to measure, while at the same time they are time and resource consuming. Metrics need also to be continuously updated. A possible solution to some of those problems is to automate the process of gathering and assessing those metrics. In this research the metrics that evaluate the complexity of Object Oriented Relational Database (ORDB) are composed of the object oriented metrics and relational database metrics. This research is based on common theoretical calculations and formulations of ORDB metrics proposed by database experts. A tool is developed that takes the ORDB schema as an input and then collects several database structural metrics. Based on those proposed and gathered metrics, a study is conducted and showed that such metrics' assessment can be very useful in assessing the database complexity.

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Assessing the Object-level behavioral complexity in Object Relational Databases

Cited by 9 publications

References 11 publications

Data Types Diversity Quantitative Indicators in Relational Databases Physical Models

Data Types Diversity Quantitative Indicators in Relational Databases Physical Models

Type-level access pattern view: Enhancing prefetching performance using the iterative and recursive patterns

Automating the Collection of Object Relational Database Metrics

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